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CITATION.cff
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cff-version: 1.2.0
message: If you use this software, please cite it using the metadata provided below.
type: software
title: 'Pyunicorn: Unified Complex Network and Recurrence Analysis Toolbox'
version: 0.8.0
date-released: '2024-09-17'
abstract: pyunicorn (Unified Complex Network and RecurreNce analysis toolbox) is an
object-oriented Python package for the advanced analysis and modeling of complex networks.
Beyond the standard measures of complex network theory (such as degree, betweenness and
clustering coefficients), it provides some uncommon but interesting statistics like
Newman's random walk betweenness. pyunicorn also provides novel node-weighted (node
splitting invariant) network statistics, measures for analyzing networks of
interacting/interdependent networks, and special tools to model spatially embedded complex
networks.
Moreover, pyunicorn allows one to easily construct networks from uni- and multivariate
time series and event data (functional/climate networks and recurrence networks). This
involves linear and nonlinear measures of time series analysis for constructing functional
networks from multivariate data (e.g., Pearson correlation, mutual information, event
synchronization and event coincidence analysis). pyunicorn also features modern techniques
of nonlinear analysis of time series (or pairs thereof), such as recurrence quantification
analysis (RQA), recurrence network analysis and visibility graphs.
pyunicorn is fast, because all costly computations are performed in compiled C code. It can
handle large networks through the use of sparse data structures. The package can be used
interactively, from any Python script, and even for parallel computations on large cluster
architectures.
authors:
- family-names: Donges
given-names: Jonathan
email: donges@pik-potsdam.de
orcid: 0000-0001-5233-7703
- family-names: Heitzig
given-names: Jobst
email: jobst.heitzig@pik-potsdam.de
orcid: 0000-0002-0442-8077
- family-names: Beronov
given-names: Boyan
email: beronov@pik-potsdam.de
orcid: 0000-0002-0900-752X
- family-names: Kühlein
given-names: Fritz
email: fritzku@pik-potsdam.de
- family-names: Bechthold
given-names: Max
email: maxbecht@pik-potsdam.de
orcid: 0009-0007-7113-4814
- family-names: Kroenke
given-names: Jonathan
email: kroenke@pik-potsdam.de
- family-names: Barfuss
given-names: Wolfram
email: barfuss@uni-bonn.de
orcid: 0000-0002-9077-5242
- family-names: Harmening
given-names: Nils
- family-names: Nascimento Silva
given-names: Filipi
- family-names: Kassel
given-names: Johannes
- family-names: Ziehbarth
given-names: Malte
- family-names: Odenweller
given-names: Adrian
- family-names: Tzinis
given-names: Efthymios
- family-names: Hotz
given-names: Ronja
license: GPL-3
repository-code: https://github.com/pik-copan/pyunicorn
preferred-citation:
type: article
title: 'Unified functional network and nonlinear time series analysis for complex
systems science: The pyunicorn package'
authors:
- family-names: Donges
given-names: Jonathan
email: donges@pik-potsdam.de
orcid: 0000-0001-5233-7703
- family-names: Heitzig
given-names: Jobst
email: jobst.heitzig@pik-potsdam.de
orcid: 0000-0002-0442-8077
- family-names: Beronov
given-names: Boyan
email: beronov@pik-potsdam.de
orcid: 0000-0002-0900-752X
- family-names: Wiedermann
given-names: Marc
orcid: 0000-0001-9869-3789
- family-names: Runge
given-names: Jakob
- family-names: Feng
given-names: Quing Yi
- family-names: Tupikina
given-names: Liubov
- family-names: Stolbova
given-names: Veronika
- family-names: Donner
given-names: Reik V.
email: redonner@pik-potsdam.de
orcid: 0000-0001-7023-6375
- family-names: Marwan
given-names: Norbert
email: marwan@pik-potsdam.de
orcid: 0000-0003-1437-7039
- family-names: Dijkstra
given-names: Henk A.
- family-names: Kurths
given-names: Jürgen
email: juergen.kurths@pik-potsdam.de
orcid: 0000-0002-5926-4276
abstract: We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis
toolbox) open source software package for applying and combining modern methods of data
analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn
is a fully object-oriented and easily parallelizable package written in the language Python. It
allows for the construction of functional networks such as climate networks in climatology or
functional brain networks in neuroscience representing the structure of statistical
interrelationships in large data sets of time series and, subsequently, investigating this
structure using advanced methods of complex network theory such as measures and models for
spatial networks, networks of interacting networks, node-weighted statistics, or network
surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex
systems as recorded in uni- and multivariate time series from a non-traditional perspective by
means of recurrence quantification analysis, recurrence networks, visibility graphs, and
construction of surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.
doi: 10.1063/1.4934554
journal: Chaos
month: 11
issue: 11
volume: 25
year: 2015